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app (1).py

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+import gradio as gr
+from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, Blip2ForConditionalGeneration, VisionEncoderDecoderModel, InstructBlipForConditionalGeneration
+import torch
+import open_clip
+
+from huggingface_hub import hf_hub_download
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+torch.hub.download_url_to_file('http://images.cocodataset.org/val2017/000000039769.jpg', 'cats.jpg')
+torch.hub.download_url_to_file('https://huggingface.co/datasets/nielsr/textcaps-sample/resolve/main/stop_sign.png', 'stop_sign.png')
+torch.hub.download_url_to_file('https://cdn.openai.com/dall-e-2/demos/text2im/astronaut/horse/photo/0.jpg', 'astronaut.jpg')
+
+git_processor_large_coco = AutoProcessor.from_pretrained("microsoft/git-large-coco")
+git_model_large_coco = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco").to(device)
+
+blip_processor_large = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+blip_model_large = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(device)
+
+blip2_processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-6.7b-coco")
+blip2_model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-6.7b-coco", device_map="auto", load_in_4bit=True, torch_dtype=torch.float16)
+
+instructblip_processor = AutoProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b")
+instructblip_model = InstructBlipForConditionalGeneration.from_pretrained("Salesforce/instructblip-vicuna-7b", device_map="auto", load_in_4bit=True, torch_dtype=torch.float16)
+
+def generate_caption(processor, model, image, tokenizer=None, use_float_16=False):
+    inputs = processor(images=image, return_tensors="pt").to(device)
+
+    if use_float_16:
+        inputs = inputs.to(torch.float16)
+    
+    generated_ids = model.generate(pixel_values=inputs.pixel_values, num_beams=3, max_length=20, min_length=5) 
+
+    if tokenizer is not None:
+        generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    else:
+        generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+   
+    return generated_caption
+
+
+def generate_caption_blip2(processor, model, image, replace_token=False):    
+    prompt = "A photo of"
+    inputs = processor(images=image, text=prompt, return_tensors="pt").to(device=model.device, dtype=torch.float16)
+
+    generated_ids = model.generate(**inputs,
+                                   num_beams=5, max_length=50, min_length=1, top_p=0.9,
+                                   repetition_penalty=1.5, length_penalty=1.0, temperature=1)
+    if replace_token:
+        # TODO remove once https://github.com/huggingface/transformers/pull/24492 is merged
+        generated_ids[generated_ids == 0] = 2 
+    
+    return processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+
+def generate_captions(image):
+    caption_git_large_coco = generate_caption(git_processor_large_coco, git_model_large_coco, image)
+
+    caption_blip_large = generate_caption(blip_processor_large, blip_model_large, image)
+
+    caption_blip2 = generate_caption_blip2(blip2_processor, blip2_model, image).strip()
+
+    caption_instructblip = generate_caption_blip2(instructblip_processor, instructblip_model, image, replace_token=True)
+
+    return caption_git_large_coco, caption_blip_large, caption_blip2, caption_instructblip
+
+   
+examples = [["cats.jpg"], ["stop_sign.png"], ["astronaut.jpg"]]
+outputs = [gr.outputs.Textbox(label="Caption generated by GIT-large fine-tuned on COCO"), gr.outputs.Textbox(label="Caption generated by BLIP-large"), gr.outputs.Textbox(label="Caption generated by BLIP-2 OPT 6.7b"), gr.outputs.Textbox(label="Caption generated by Swin Transformer with GPT-2"), ] 
+
+title = "Interactive demo: comparing image captioning models"
+description = "Gradio Demo to compare GIT, BLIP, BLIP-2 and InstructBLIP, 4 state-of-the-art vision+language models. To use it, simply upload your image and click 'submit', or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://huggingface.co/docs/transformers/main/model_doc/blip' target='_blank'>BLIP docs</a> | <a href='https://huggingface.co/docs/transformers/main/model_doc/git' target='_blank'>GIT docs</a></p>"
+
+interface = gr.Interface(fn=generate_captions, 
+                         inputs=gr.inputs.Image(type="pil"),
+                         outputs=outputs,
+                         examples=examples, 
+                         title=title,
+                         description=description,
+                         article=article, 
+                         enable_queue=True)
+interface.launch(debug=True,share = True)

requirements (1).txt

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+git+https://github.com/huggingface/transformers.git@main
+torch
+open_clip_torch
+accelerate
+bitsandbytes
+scipy
+gradio